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IPLUSO 25645

Internet of Things

Automation and Computer Systems
  • ApresentaçãoPresentation
    he Internet of Things course unit is positioned within the domain of integrating embedded systems, communication networks, and data processing, playing a relevant role in the Automation and Computer Systems degree. The course covers the complete lifecycle of an IoT system, from data acquisition through sensors and actuators to communication, processing, and data analysis. Its scope focuses on the development and integration of IoT solutions applied to industrial, environmental, and urban contexts, with emphasis on low-power distributed systems. The main areas include IoT communication technologies, data integration platforms, and the fundamentals of time-series analysis. The course follows a practical approach, integrating theory, laboratory work, and projects, and prepares students for academic and professional contexts in intelligent systems.
  • ProgramaProgramme
    Introduction to the Internet of Things, including fundamental concepts, evolution, and main application domains. IoT architectures and system components, such as sensors, actuators, embedded systems, and gateways. Communication technologies for IoT, with emphasis on low-power and long-range wireless networks, particularly LoRa and LoRaWAN, their operating principles, parameters, and performance metrics. Device and data integration into IoT platforms. Acquisition, pre-processing, and analysis of sensor-based time series. Application of statistical filters for noise reduction and anomaly detection. Introduction to machine learning applied to IoT data, including supervised methods and basic concepts of Reinforcement Learning for adaptive decision support. Security, privacy, and reliability aspects in IoT systems. Development of laboratory activities and an integrative practical project combining communication, data processing, and intelligent analysis.
  • ObjectivosObjectives
    By the end of this course unit, students will acquire fundamental knowledge of the Internet of Things, including IoT architectures, sensors, embedded systems, and wireless communication technologies, with emphasis on low-power solutions. Students will understand the integration of devices into data collection and processing platforms. Students will develop skills in acquiring, processing, and analysing sensor data using programming tools and basic time-series analysis techniques, as well as introductory competences in applying statistical methods and machine learning techniques to support monitoring in IoT systems. The course also promotes critical thinking, problem solving, and teamwork, preparing students for academic and professional contexts in automation and intelligent systems.
  • BibliografiaBibliography
    Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010 McEwen, A., & Cassimally, H. (2014). Designing the Internet of Things. Chichester, UK: John Wiley & Sons. ISBN 978-1-118-43062-0 Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095 Centenaro, M., Vangelista, L., Zanella, A., & Zorzi, M. (2016). Long-range communications in unlicensed bands: The rising stars in the IoT and smart city scenarios. IEEE Wireless Communications, 23(5), 60–67. https://doi.org/10.1109/MWC.2016.7721743  
  • MetodologiaMethodology
    The course adopts active, student-centred methodologies, promoting problem-based and project-based learning within the Internet of Things context. The combination of theoretical–practical and laboratory sessions enables the immediate application of the addressed concepts. Guided Python-based laboratories are used with synthetic datasets and reproducible analysis for IoT data processing and interpretation. The development of an integrative project supports knowledge consolidation, critical thinking, and teamwork. Digital learning platforms are used to support continuous monitoring, material sharing, and formative feedback throughout the semester.
  • LínguaLanguage
    Português
  • TipoType
    Semestral
  • ECTS
    4
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não